Most prior value relevance studies examine goodwill impairment shortly after the introduction of the impairment method and find value relevance. This study contributes with surprising results stating that goodwill impairment losses are non- value relevant for investors on the Swedish equity market. Similar results are also presented by (Laghi et al., 2013), but for different equity markets in Europe. Both this study and Laghi et al., (2013) use time periods that stretch beyond the transition phase. More studies examining value relevance of goodwill impairment in the later years would therefore be beneficial to see if the value relevance of goodwill impairment has changed. Moreover, the study of Laghi et al., (2013) indicate that it can have a vital importance if one includes or excludes zero goodwill impairments. This could also be an area to examine further as this study excludes the potential signaling value of zero goodwill impairments.
Apart from the overall results on value relevance, this study suggests that financial leverage impacts the value relevance of goodwill impairment losses. Therefore, it would be beneficial to further explore the area of value relevance of goodwill impairment losses with focus on leverage. The results of this study are similar to the findings of Zang (2008). Both studies also adopt a similar multivariate OLS regression. It could therefore be interesting to verify the results by using a regression estimator based on the Ohlson valuation framework. Moreover, the results of this study suggest that debt covenants play an important role in determining the value relevance of goodwill impairment losses. However, the study contributes also with an additional theory that together with the debt covenant theory could explain why the market reaction becomes more negative with leverage. It would be interesting to test the two theories against each other and examine to
what extent they explain how leverage impacts the value relevance of goodwill impairment losses.
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APPENDICES
Appendix 1
OLS requires that the variance in the error term is constant. If the variance in the error term is not constant, heteroskedacity is present. The existence of heteroskedasticity can be examined with a White test. The null hypothesis of the White test states that homoscedasticity (constant variance in the error term) exists while the alternative hypothesis states that heteroskedacity exists (Wooldridge, 2014). Three White tests are carried out for the value relevance regression, the leverage regression and the robustness test regression and the null hypothesis is rejected if p < 0.01.
Table 8. The White heteroskedasticity test for the value relevance regression
Source Chi2 df p
Heteroskedasticity 134.66 35 0.0000
Skewness 30.17 7 0.0001
Kurtosis 2.19 1 0.1386
Total 167.03 43 0.0000
Notes: The null hypothesis is rejected at the 1% significance level and thus,
heteroskedacity exists.
Table 9. The White heteroskedasticity test for the leverage regression
Source Chi2 df p
Heteroskedasticity 124.33 50 0.0000
Skewness 22.27 9 0.0081
Kurtosis 5.61 1 0.0178
Total 152.21 60 0.0000
Notes: The null hypothesis is rejected at the 1% significance level and thus,
Table 10. The White heteroskedasticity test for the robustness regression Source Chi2 df p Heteroskedasticity 42.22 35 0.1872 Skewness 13.65 7 0.0577 Kurtosis 6.84 1 0.0089 Total 62.71 43 0.0264
Notes: The null hypothesis cannot be rejected at any significance level and thus,
homoscedasticity exists.
Appendix 2
The VIF-score measures to what extent the variance in the regression coefficients is affected by correlation among the independent variables. A lower VIF-score is always preferable, and one rule of thumb is that the VIF-score should not exceed 10 (Wooldridge, 2014). The VIF-scores are low for all regressions indicating that multicollinearity is no issue in any of the regressions.
Table 11. VIF-test for the independent variables in the value relevance regression
Variable VIF 1/VIF
GWIL 2.01 0.498340 ∆ROE 1.71 0.583220 LTG 1.22 0.821514 Beta 1.62 0.616246 SIZE 1.82 0.550353 MTB 1.03 0.969232 MOM 1.18 0.848388 Mean VIF 1.51
Table 12. VIF-test for the independent variables in the leverage regression
Variable VIF 1/VIF
FLEV x GWIL 1.45 0.688304 GWIL 2.05 0.488946 ∆ROE 1.75 0.572742 LTG 1.10 0.912616 Beta 1.78 0.562974 SIZE 2.10 0.476201 MOM 1.26 0.793365 MTB 1.20 0.836229 FLEV 1.41 0.711389 Mean VIF 1.56
Table 13. VIF-test for the independent variables in the robustness regression
Variable VIF 1/VIF
GWIL 2.03 0.493536 ∆ROE 1.59 0.630485 LTG 1.16 0.858910 Beta 1.47 0.678144 SIZE 1.72 0.581338 MOM 1.71 0.585487 MTB 1.37 0.729331 Mean VIF 1.58
In addition to the VIF-test, multicollinearity is also examined with a Pearson’s correlation matrix. The Pearson’s correlation matrix finds no strong correlation between any of the independent variables. Hence, multicollinearity is no issue for the value relevance and the leverage regressions.
Table 14. Pearson’s correlation matrix for the regression variables
CAR GWIL ROE LTG BETA SIZE MTB MOM FLEV FLEVxGWIL
CAR 1 GWIL -0.15 1 ROE 0.0788 -0.621 1 LTG 0.0363 0.0232 0.0109 1 BETA -0.063 -0.175 0.1164 -0.063 1 SIZE 0.1786 -0.443 0.3397 -0.043 0.5779 1 MTB 0.2443 -0.089 0.0251 0.1398 0.1663 0.266 1 MOM 0.2304 -0.181 0.2537 0.0402 -0.088 0.191 0.2532 1 FLEV -0.091 -0.189 0.023 -0.228 0.2837 0.075 0.0823 -0.099 1 FLEVxGWIL -0.44 0.3619 -0.354 -0.112 -0.013 -0.27 -0.085 -0.294 0.284 1
Appendix 3
Two separate OLS regressions are carried out to verify the results of the leverage regression. The goodwill impairment loss sample is divided into two subsamples based on financial leverage. The median debt equity ratio of 1.52 is used as a cut-off point to separate the two leverage groups. The group with low financial leverage consists of firms with a debt equity ratio less than 1.52 and the group with high financial leverage consists of firms with a debt equity ratio above 1.52.
The results of the two OLS regressions indicate that goodwill impairment losses in firms with low financial leverage are non-value relevant, while goodwill impairment losses cause a negative market reaction and are value relevant at the 5% significance level in firms with high financial leverage. The results are therefore in line with the results of the leverage regression.
Table 15. Alternative leverage regression (equation 13).
Low financial leverage High financial leverage
Variable Coefficient P-value T statistics Coefficient P-value T statistics
GWIL -0.03071 0.687 -0.40 -0.8355647** 0.040 -2.09 ∆ROE --0.02956 0.298 -1.05 -0.0291459 0.709 -0.37 LTG 0.01437 0.956 0.06 -0.4527288 0.179 -1.36 BETA 0.005989 0.781 0.28 -0.0753048** 0.040 -2.08 SIZE -0.00147 0.659 -0.44 0.0093202 0.165 1.40 MTB 0.009352 0.677 0.42 0.0097306** 0.036 2.13 MOM 0.007254 0.103 1.65 0.0283953 0.436 0.78 Intercept -0.00434 0.925 -0.09 -0.0882698 0.321 -1.00 N 94 94 Median DE 0.89 2.33 R2 0.0535 0.3589 F value 0.69 3.07***
Notes: *** significant at the 1% level, ** significant at the 5% level and * significant at
the 10% level. Robust standard errors are used for the high financial leverage regression. ROE, LTG and BETA are winsorized to the 2.50 and 97.50 percentiles.
Appendix 4
Table 16. The sample firms with reported goodwill impairment losses for years 2005-2013
AB SKF GUNNEBO AB* PROACT IT GROUP AB
ABB LTD HEMTEX AB* PROFFICE AB
ACANDO AB HOLMEN AB RATOS AB
ADDNODE GROUP AB IAR SYSTEMS REDERI AB TRANS*
AF AB ICA GRUPPEN AB RNB RETAIL*
ALFA LAVAL AB INDUTRADE AB SAAB AB
ANOTO GROUP AB INTELLECTA AB SANDVIK AB
ASPIRO AB INTRUM JUSTITIA AB* SECURITAS AB
ATLAS COPCO AB INVESTMENT AB LATOUR* SEMCON AB
ATRIUM LJUNGBERG AB* INVESTMENT AB KINNEVIK SKANDINAVISKA ENSK
AVANZA BANK KLOVERN AB* SKANSKA AB
Bilia LINDAB INTER* SSAB SVENSKT STAL AB
BILLERUDKORSNAS PUBL LUNDIN PETROLEUM AB STOCKWIK FORVALT
BIOTAGE AB MIDSONA STUDSVIK AB
BLACK EARTH* MODERN TIMES GRP MTG* SWECO AB*
BONG LJUNGDAHL AB* MSC KONSULT AB SWEDBANK AB
BURE EQUITY AB MULTIQ INTL AB SVENSKA CELLULOSA AB
CYBERCOM* NCC AB SYSTEMAIR AB*
DIGITAL VISION AB NOKIA CORP* TELE2 AB*
DUROC AB NORDNET SECURITIES TELIASONERA AB*
ELANDERS AB OEM-INTERNATIONAL AB TIETO OYJ
ENIRO AB* ORTIVUS AB TRADEDOUBLER AB*
FEELGOOD SVENSKA AB PARTNERTECH AB TRELLEBORG AB*
G5 ENTERTAINMENT PEAB AB* VENUE RETAIL GROUP
GETINGE AB POOLIA AB VOLVO AB
GLOBAL HEALTH PART* PREVAS AB
Notes: The firms marked with an asterisk have had debt covenants for at least one